Display panels or monitors continue to increase in resolution, such as 1080p, 4k×2k, etc. Many image sources have a lower resolution than the displays. When the panel displays these sources in a display with higher resolution, traditional scaling techniques do not produce high fidelity results with rich details and sharp edges. Examples of these traditional techniques include one-dimensional poly phase scaling (1D scaler), bilinear interpolation, bi-cubic interpolation, edge-guided (2D) scaling, etc. Super resolution techniques offer a solution to bridge the gap between advanced, higher resolution displays and lower resolution video sources.
Multi-image super resolution techniques construct a high resolution image from multiple low resolution images by fusing information among them. These algorithms can bring some details with smaller ratios of magnification in zooming. When larger ratios of magnification are needed, they cannot give enough details. Further, even at smaller ratios, if the motion is not in the right range, the ability of these techniques to reconstruct high frequency details suffers. These techniques also require many hardware resources and complex logic.
Different types of single frame super resolution (SFSR) techniques exist. Typically, these processes may involve using comparisons between the original low resolution image and various scaled versions of the low resolution image to generate the high resolution image at the desired magnification, or higher resolution. The processes may generate intermediate layers of image data at resolutions between the original input resolution and the desired high resolution, and may generate image layers at resolutions below the original image resolution. These layers are searched for matches in the image data and corresponding data may be copied between one of the intermediate layers and the desired high resolution image layer. This process is called patch matching.
However, patch matching has high computational requirements as it involves searching algorithms and detailed data analysis. Techniques that can lower the computational requirements while maintaining good image quality for the high resolution image would be desirable.